Wastewater networks are critical components of intelligent urban environments, yet they often operate with sparse sensing due to installation and maintenance constraints. This work investigates how graph-theoretic properties can guide effective sensor placement to support AI-driven monitoring in large-scale sewer systems. We evaluate sensor selection strategies based on betweenness and closeness centrality and introduce a coverage-aware algorithm that improves spatial distribution while preserving structural importance. Using a real wastewater network from the Netherlands and a GNN-based water-level estimation model, we assess performance under extreme sparsity conditions$(70-98 \%$masked nodes). Results show that pure centrality metrics often cluster sensors in dense regions, leading to uneven estimation performance, whereas our coverage-aware extension significantly improves network-wide MAE and stability. These findings highlight the importance of integrating topology and coverage considerations for intelligent environmental monitoring in smart cities.

In the dynamic landscape of machine learning applications on streaming data, the constant evolution of models and input data complicates optimal model deployment. The static selection of a model risks suboptimal performance as data patterns evolve, while frequent redeployments increase operational costs. This paper proposes a self-adaptive system that autonomously selects interchangeable models for processing streaming data while balancing the tradeoff of performance and redeployment frequency. Inspired by the MAPE-K reference model, our approach utilizes an adaptive model selection control loop to continuously monitor model performance on production and experimental data. "what-if" environments are introduced to collect additional experimental data, simulating production-like scenarios. A selection algorithm that employs two distinct adaptation policies is introduced that strategically plans the selection of the most suitable module for upcoming data. Leveraging a learning-based method, we improve the efficiency of our system by recognizing the patterns of selection eliminating the need for further experimental data collection. Empirical evaluation on an energy forecasting use case spans over 16 years of data demonstrates a substantial reduction in errors up to 34% compared to the best static selection, affirming the proposed framework’s effectiveness. Our findings reveal the potential to discontinue experimental "what-if" analyses with just 12% of historical data, which underlines the practicality of our adaptive strategy on a long-lasting task.

Abstract Conventional digital twins (DT) for critical infrastructures are widely used to model and simulate the system’s state. But fundamental environment changes bring challenges for DT adaptation to new conditions, leading to a progressively decreasing correspondence of the DT to its physical counterpart. This paper introduces the DiTEC system, a Digital Twin for Evolutionary Changes in Water Distribution Networks (WDN). This framework combines novel techniques, including semantic rule learning, graph neural network-based state estimation, and adaptive model selection, to ensure that changes are adequately detected, processed and the DT is updated to the new state. The DiTEC system is tested on the Dutch Oosterbeek region WDN, with results showing the superiority of the approach compared to traditional methods.

Constraint satisfaction problems are, in general, NP-complete problems, meaning that the computational complexity increases exponentially with the size of the problem in the worst case, under the assumption that P does not equal NP. The structure of a problem heavily influences its computational complexity, however, and problems with a restricted structure constitute one of the general classes of tractable problems. This paper explores the community structure of constraint satisfaction problems, a type of structure already found to be important for SAT problems that is inherent to certain real-world domains. The community structure of the instances of the MiniZinc Challenge of 2019 was identified, and its correlation with the search times of four state-of-the-art solvers as well as with the tree-width of the instances was analysed. The results reveal the strong community structure of many of the instances, although the strength of the community structure seems to only marginally affect the search times. On the other hand, a strong correlation between the community structure and the tree-width is observed, where stronger community structure suggests better decomposability. Taking community structure into account more explicitly during the search process may, therefore, allow constraints solvers to solve problems with strong community structure more efficiently.

Building managers need effective tools to improve occupants’ experiences considering constraints of energy efficiency. Current building management systems are limited to coordinating device services in simple and prefixed situations. Think of an office with lights offering services, such as turn on a light, which are invoked by the system to automatically control the lights. In spite of the evident potential for energy saving, the office occupants often end up in the dark, they have too much light when working with computers, or unnecessary lights are turned on. The office is thus not aware of the occupants’ presence nor anticipates their activities. Our proposal is to coordinate services while anticipating occupant activities with sufficient accuracy. Finding and composing services that will support occupant activities is however a complex problem. The high number of services, the continuous transformation of buildings, and the various building standards imply a search through a vast number of possible contextual situations every time occupants perform activities. Our solution to this building coordination problem is based on Hierarchical Task Network (HTN) planning in combination with activity recognition. While HTN planning provides powerful means for composing services automatically, activity recognition is needed to identify occupant activities as soon as they occur. The output of this combination is a sequence of services that needs to be executed under the uncertainty of building environments. Our solution supports continuous context changes and service failures by using an advanced orchestration strategy. We design, implement and deploy a system in two cases, namely offices and a restaurant, in our own office building at the University of Groningen. We show energy savings in the order of 80% when compared to manual control in both cases, and 60% when compared to using only movement sensors. Moreover, we show that one can save a figure of €600 annually for the electricity costs of the restaurant. We use a survey to evaluate the experience of restaurant occupants. The majority of them are satisfied with the solution and find it useful. Finally, the technical evaluation provides insights into the efficiency of our system.

We propose the use of HTN planning for risk-sensitive planning domains. We suggest utility functions that reflect the risk attitude of compound tasks, and adapt a best-first search algorithm to take such utilities into account.

Flexible and easily adjustable reasoning mechanisms are essential for rendering sensor and actuator rich indoor environments smart. Constraint-based solutions are a suitable approach for such systems. We propose an approach that allows users to specify the rules for a building's behavior, and uses context information to represent the rules and environment as a dynamic constraint satisfaction problem. The dependency graph data structure allows to find efficiently only the affected parts of the environment, thus minimizing the computational efforts after every event. We evaluate the system on a building implementation as a living lab, and with performance experiments. The testing proves the high efficiency and applicability of the approach for dynamic control of smart environments.

Domotics, concerned with the realization of intelligent home environments, is a novel field which can highly benefit from solutions inspired by service-oriented principles to enhance the convenience and security of modern home residents. In this work, we present an architecture for a smart home, starting from the lower device interconnectivity level up to the higher application layers that undertake the load of complex functionalities and provide a number of services to end-users. We claim that in order for smart homes to exhibit a genuinely intelligent behavior, the ability to compute compositions of individual devices automatically and dynamically is paramount. To this end, we incorporate into the architecture a composition component that employs artificial intelligence domain-independent planning to generate compositions at runtime, in a constantly evolving environment. We have implemented a fully working prototype that realizes such an architecture, and have evaluated it both in terms of performance as well as from the end-user point of view. The results of the evaluation show that the service-oriented architectural design and the support for dynamic compositions is quite efficient from the technical point of view, and that the system succeeds in satisfying the expectations and objectives of the users.

Web Service (WS) domains constitute an application field where automated planning can significantly contribute towards achieving customisable and adaptable compositions. Following the vision of using domain-independent planning and declarative complex goals to generate compositions based on atomic service descriptions, we apply a planning framework based on Constraint Satisfaction techniques to a domain consisting of WSs with diverse functionalities. One of the key requirements of such domains is the ability to address the incomplete knowledge problem, as well as recovering from failures that may occur during execution. We propose an algorithm for interleaving planning, monitoring and execution, where continual planning via altering the CSP is performed, under the light of the feedback acquired at runtime. The system is evaluated against a number of scenarios including real WSs, demonstrating the leverage of situations that can be effectively tackled with respect to previous approaches.

Pervasive context-aware systems base their responses on information about the environment collected from ubiquitous sensors. The inevitable drawback of such systems is that raw data collected from sensors is often noisy, corrupted, and imprecise. Erroneous sensor readings create uncertainties and ambiguous interpretations. Thus creating an interpretation challenge for the context-aware system that needs to reason about possible states of only partially observable subjects. We propose a mechanism for pervasive context-aware systems to process the information gathered from sensors so to obtain knowledge about possible environment states. This includes both the ability to reason about a situation with incomplete knowledge and to cope with erroneous contexts. We present a probabilistic approach to reason about the likelihood of each particular situation, state of a variable, and variable interdependence. The evaluation shows that the proposed approach is applicable to real-time context inference problems.

Mining and analyzing data from social networks can be difficult because of the large amounts of data involved. Such activities are usually very expensive, as they require a lot of computational resources. With the recent success of cloud computing, data analysis is going to be more accessible due to easier access to less expensive computational resources. In this work we propose to use cloud computing services as a possible solution for analysis of large amounts of data. As a source for a large data set, we propose to use Twitter, yielding a graph with 50 million nodes and 1.8 billion edges. In this paper, we use computation of PageRank on Twitter's social graph to investigate whether or not cloud computing, and Amazon cloud services in particular, can make these tasks more feasible and, as a side effect, whether or not PageRank provides a good ranking of Twitter users.

Business processes that span organizational borders describe the interaction between multiple parties working towards a common objective. They also express business rules that govern the behavior of the process and account for expressing changes reflecting new business objectives and new market situations. We developed a service request language and support framework that allow users to formulate their requests against standard business processes.19 In this paper, we extend the approach by presenting a framework capable of automatically associating business rules with relevant processes involved in a user request. This framework plans and monitors the execution of the request and assertions against services underlying these processes. Definitions and classifications of business rules (named assertions in the paper) are given together with an assertion language for expressing them. The framework is able to handle the non-determinism typical for service-oriented computing environments and it is based on the interleaving of planning and execution. Interestingly, the language is able to express both functional and non-functional aspects of the assertions.

Business processes that span organizational borders describe the interaction between multiple parties working towards a common objective. They also express business rules that govern the behavior of the process and account for expressing changes reflecting new business objectives and new market situations.